Cargando…
Rough-Fuzzy Pattern Recognition : Applications in Bioinformatics and Medical Imaging.
Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processingEmphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing t...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
John Wiley & Sons,
2012.
|
| Colección: | Wiley series on bioinformatics.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- ROUGH-FUZZY PATTERN RECOGNITION; CONTENTS; Foreword; Preface; About the Authors; 1 Introduction to Pattern Recognition and Data Mining; 1.1 Introduction; 1.2 Pattern Recognition; 1.2.1 Data Acquisition; 1.2.2 Feature Selection; 1.2.3 Classification and Clustering; 1.3 Data Mining; 1.3.1 Tasks, Tools, and Applications; 1.3.2 Pattern Recognition Perspective; 1.4 Relevance of Soft Computing; 1.5 Scope and Organization of the Book; References; 2 Rough-Fuzzy Hybridization and Granular Computing; 2.1 Introduction; 2.2 Fuzzy Sets; 2.3 Rough Sets; 2.4 Emergence of Rough-Fuzzy Computing.
- 2.4.1 Granular Computing2.4.2 Computational Theory of Perception and f -Granulation; 2.4.3 Rough-Fuzzy Computing; 2.5 Generalized Rough Sets; 2.6 Entropy Measures; 2.7 Conclusion and Discussion; References; 3 Rough-Fuzzy Clustering: Generalized c-Means Algorithm; 3.1 Introduction; 3.2 Existing c-Means Algorithms; 3.2.1 Hard c-Means; 3.2.2 Fuzzy c-Means; 3.2.3 Possibilistic c-Means; 3.2.4 Rough c-Means; 3.3 Rough-Fuzzy-Possibilistic c-Means; 3.3.1 Objective Function; 3.3.2 Cluster Prototypes; 3.3.3 Fundamental Properties; 3.3.4 Convergence Condition; 3.3.5 Details of the Algorithm.
- 3.3.6 Selection of Parameters3.4 Generalization of Existing c-Means Algorithms; 3.4.1 RFCM: Rough-Fuzzy c-Means; 3.4.2 RPCM: Rough-Possibilistic c-Means; 3.4.3 RCM: Rough c-Means; 3.4.4 FPCM: Fuzzy-Possibilistic c-Means; 3.4.5 FCM: Fuzzy c-Means; 3.4.6 PCM: Possibilistic c-Means; 3.4.7 HCM: Hard c-Means; 3.5 Quantitative Indices for Rough-Fuzzy Clustering; 3.5.1 Average Accuracy, a Index; 3.5.2 Average Roughness, o Index; 3.5.3 Accuracy of Approximation, a* Index; 3.5.4 Quality of Approximation, g Index; 3.6 Performance Analysis; 3.6.1 Quantitative Indices; 3.6.2 Synthetic Data Set: X32.
- 3.6.3 Benchmark Data Sets3.7 Conclusion and Discussion; References; 4 Rough-Fuzzy Granulation and Pattern Classification; 4.1 Introduction; 4.2 Pattern Classification Model; 4.2.1 Class-Dependent Fuzzy Granulation; 4.2.2 Rough-Set-Based Feature Selection; 4.3 Quantitative Measures; 4.3.1 Dispersion Measure; 4.3.2 Classification Accuracy, Precision, and Recall; 4.3.3 k Coefficient; 4.3.4 b Index; 4.4 Description of Data Sets; 4.4.1 Completely Labeled Data Sets; 4.4.2 Partially Labeled Data Sets; 4.5 Experimental Results; 4.5.1 Statistical Significance Test; 4.5.2 Class Prediction Methods.
- 4.5.3 Performance on Completely Labeled Data4.5.4 Performance on Partially Labeled Data; 4.6 Conclusion and Discussion; References; 5 Fuzzy-Rough Feature Selection using f -Information Measures; 5.1 Introduction; 5.2 Fuzzy-Rough Sets; 5.3 Information Measure on Fuzzy Approximation Spaces; 5.3.1 Fuzzy Equivalence Partition Matrix and Entropy; 5.3.2 Mutual Information; 5.4 f -Information and Fuzzy Approximation Spaces; 5.4.1 V -Information; 5.4.2 Ia-Information; 5.4.3 Ma-Information; 5.4.4 ca-Information; 5.4.5 Hellinger Integral; 5.4.6 Renyi Distance; 5.5 f -Information for Feature Selection.